How to compare football players by statistics?

What statistical indicators to use for comparing football players

Correctly comparing players based on statistics starts with understanding which metrics truly reflect a player’s contribution to the game. It is important to differentiate indicators by roles: forwards, midfielders, defenders, and goalkeepers are evaluated using different sets of numbers. At the same time, some metrics are universal: minutes on the field, number of starts, involvement in goal actions, discipline. For objectivity, statistics should be adjusted to the format «per 90 minutes» and consider the context: tournament level, team style, frequency of substitutions.

For attacking players, goals and assists remain key, but they alone reveal little. It is important to analyze the number of shots, shot accuracy, involvement in building attacks (passes leading to shots, touches in the penalty area, participation in quick breaks). This way, one can distinguish a forward who relies on teammates and finishes chances from a player who generates dangerous situations on their own. It is useful to compare conversion rates of shots to goals and consistency across seasons, rather than just one successful stretch.

Midfielders and defenders are logically evaluated through ball control and off-ball work. For defensive midfielders and central midfielders, the accuracy of passes, number of progressive passes, passes into the final third, tackles, and interceptions are important. For full-backs and wing-backs, this includes crosses, deliveries into the penalty area, and won duels in wide areas. Goalkeepers should be compared based on saves, handling, and footwork, but always in relation to the volume of shots on their goal and the quality of the team’s defense. The richer the initial statistics from the API, the deeper and more accurate such comparisons will be.

How to choose a football statistics API for analyzing and comparing players

When choosing a data provider for football analytics, it is critical to assess not only the price but also the depth of coverage. The API should provide information about sports and tournaments, player profiles, match lineups, event feeds, and, if possible, advanced match statistics. In the specification Sports data API api-sport.ru this is implemented through a single REST interface: you receive a list of sports, categories, tournaments, matches, and teams, and then lineups and statistics. This approach allows for player comparisons both within a single championship and across different leagues.

Attention should be paid to the structure and quality of the documentation. Important signals include the presence of descriptions for all entities (sport, tournament, match, team, player), clearly defined filtering parameters, and examples of requests. In the case of api-sport.ru, an endpoint is available /v2/sport to obtain a list of available sports, detailed schemas for the Player, Team, Match entities, as well as extended fields like matchStatistics и oddsBase for the match. This allows not only to compare football players with each other but also to link their influence to the dynamics of bookmaker odds.

A separate criterion is the technological sophistication of the service. For serious projects, stability, a predictable change history (changelog), convenient API key authorization, and development plans are important. On api-sport.ru new opportunities are actively being developed: new sports are being added, fields in match statistics are being expanded, support for WebSocket for streaming live data and AI-based tools is planned. This development vector is important if you are building a long-term product: from scouting platforms and fantasy to betting analytics and media applications.

How to obtain player statistics through an API: a step-by-step guide

To start comparing football players based on real data, you first need to gain access to the API. On the api-sport.ru platform, this is done through registration in your personal account at api-sport.ru and issuing a unique API key. This key is passed in each request in the header Authorization, which allows you to securely identify your application and control limits. After obtaining the key, the first step usually involves a request to the endpoint /v2/sport, to determine the slug of the sport (for football — football).

[вставить]

curl -X GET "https://api.api-sport.ru/v2/sport" \
  -H "Authorization: YOUR_API_KEY"

[/вставить]

Next, you select the necessary tournaments and matches. First, you can get a list of categories (countries/regions) for football through /v2/football/categories, then — tournaments within the category, after which you can filter matches by tournament, season, or team using the endpoint /v2/football/matches. The response for the match already contains data about the teams, as well as lineups and player statistics through the structure homeTeam и awayTeam, which includes the object lineup. It is from there that the analyst or developer extracts match statistics of football players and saves it in their database for subsequent comparisons.

If you need to get a list of players from a specific team without tying it to a match, use the endpoints /v2/football/teams и /v2/football/players. The first returns the team with an array of players, the second allows you to request profiles of individual footballers by their identifiers or by team ID. Based on this data, you can create player cards, calculate age, positional distribution, market value, and other metrics, and then combine them with match statistics obtained from match queries.

Examples of API requests for comparing players based on key metrics

The practical comparison begins with obtaining the profiles of the necessary players. Suppose you need to match two forwards from different leagues. First, you request their profiles by ID through /v2/football/players, to get the basic parameters: country, date of birth, position, dominant foot, height, estimated market value. These metrics allow you to form a general portrait of the footballers and normalize the comparison by age and role on the field.

[вставить]

curl -X GET "https://api.api-sport.ru/v2/football/players?ids=123456,789012" \
  -H "Authorization: YOUR_API_KEY"

[/вставить]

The next step is to extract match statistics to compare players by their contribution to the game. In the API api-sport.ru, statistics on footballers come as part of matches: you make a request to /v2/football/matches with the necessary filters (by date, tournament, team, or list of matches), and then go through the structures homeTeam.lineup.players и awayTeam.lineup.players. Inside each player object, there is a field statistics, where numerical indicators of a specific performance are stored (the set of metrics depends on the sport and the tariff configuration). Your task remains: to aggregate this data by seasons, calculate «per 90 minutes» metrics, and build custom efficiency indices.

[вставить]

const API_KEY = 'YOUR_API_KEY';
async function loadMatchPlayers(matchId) {
  const res = await fetch(`https://api.api-sport.ru/v2/football/matches/${matchId}`, {
    headers: { Authorization: API_KEY },
  });
  const match = await res.json();
  const homePlayers = match.homeTeam?.lineup?.players || [];
  const awayPlayers = match.awayTeam?.lineup?.players || [];
  return [...homePlayers, ...awayPlayers].map((p) => ({
    id: p.id,
    name: p.name,
    position: p.position,
    statistics: p.statistics,
  }));
}

[/вставить]

By combining player profiles and match statistics, you can make comparisons based on goals, involvement in goal actions, defensive actions, or any other available fields. In practice, data from the API often accumulates in its own database, after which reports and interfaces are built on top of them: from scouting tables to betting panels, where the players’ metrics correlate with the odds from the field oddsBase in matches.

How to compare football players using advanced statistics xG, xA, and heatmaps through an API

Advanced metrics like expected goals (xG), expected assists (xA), and heatmaps add depth to standard metrics. They help to understand not only the fact of a goal or a shot but also the quality of the created moment. In typical sports analytics systems, xG and xA are either provided as a separate data stream or calculated on the client side based on raw match events: information about the type of shot, distance, body part, playing conditions, etc. The sports events API serves as the foundation for such calculations, providing detailed event and match statistics to which you can already apply your own models.

It is important to understand that the availability of xG/xA and coordinates for building heatmaps depends on the specific tariff and configuration of the data provider. The basic API specification may only include aggregated indicators (shots, possession, tackles, etc.), while advanced metrics are either provided through separate endpoints or calculated independently. In the context sports API api-sport.ru You can use match event data and player statistics as input for your own machine learning models: the script retrieves data via REST, then enriches it with xG/xA and builds heatmaps in the application’s visual layer.

The practical approach looks like this: you regularly load match and player performance data via the API, store it in your database, after which an analytical layer works on top of this data. One module is responsible for calculating advanced metrics (xG, xA, pressing intensity indices, etc.), while another is responsible for generating visualizations (heatmaps, radial diagrams, action maps). This allows comparing football players not only by final goals and assists but also by expected contributions, quality of created moments, and areas of influence on the field.

How to visualize and compare player statistics based on API data

Numbers alone are poorly perceived by users, so a key step after API integration is visualization. On the client side or analytical showcase, you can turn data about football players into graphs and charts: bar charts for goals and assists, line charts for form dynamics over rounds, radial (spider charts) for comparing player profiles across 5–10 key metrics. The source of truth remains your data collector, which regularly queries the API, updates the local database, and ensures the consistency of metrics.

Technically, the process looks like this: the script requests match and player data via REST, then normalizes the statistics (for example, converting metrics to a «per 90 minutes» or «share of team actions» format). After that, the aggregated data is sent to the frontend in a convenient format. There, they are connected to visualization libraries (Chart.js, D3.js, ECharts, etc.), where each player turns into a set of data series. Thus, the end user sees not raw API responses but neat tables and graphs, where they can compare two or three football players with one click.

[вставить]

function buildRadarDataset(playerName, metrics) {
  // metrics: объект с нормализованными значениями по ключевым показателям
  return {
    label: playerName,
    data: [
      metrics.goalsPer90,
      metrics.assistsPer90,
      metrics.shotsPer90,
      metrics.duelsWonPercent,
      metrics.keyPassesPer90,
    ],
  };
}

[/вставить]

As the service evolves, you can add a live component: use the planned WebSocket support in the API to build online panels where statistics update without reloading the page. In this mode, data on touches, shots, or substitutions can appear on graphs during the match, and analysts and fans will see how the influence of a specific player changes in real time.

How to automate player comparison based on statistics using scripts and APIs

One-time API requests are useful for testing, but in real products, comparing football players should be fully automated. A typical architecture looks like this: a background script queries the API on a schedule, updates data on matches, lineups, and players, then recalculates aggregated metrics and saves them in the database. Reports, showcases, or public interfaces are built on top of this database, where users only see final ratings and comparisons, not the complex mechanics of data updates.

[вставить]

import requests
API_KEY = 'YOUR_API_KEY'
BASE_URL = 'https://api.api-sport.ru/v2/football'
headers = {"Authorization": API_KEY}

def load_matches_by_date(date_str: str):
    resp = requests.get(f"{BASE_URL}/matches", params={"date": date_str}, headers=headers)
    resp.raise_for_status()
    return resp.json().get("matches", [])

def extract_player_stats(match):
    players = []
    for side in ("homeTeam", "awayTeam"):
        team = match.get(side, {})
        for p in team.get("lineup", {}).get("players", []):
            players.append({
                "player_id": p.get("id"),
                "name": p.get("name"),
                "position": p.get("position"),
                "statistics": p.get("statistics", {}),
            })
    return players

# Далее: сохранить players в БД и пересчитать агрегаты по сезонам

[/вставить]

This approach allows you to offload all heavy logic to the backend: normalizing minutes, calculating metrics «per 90 minutes,» influence indices, custom ratings. As a result, the frontend or mobile application only works with ready aggregates and can instantly display player comparisons across dozens of metrics. Using the personal account api-sport.ru, you control API keys, monitor request statistics, and scale the infrastructure as the project grows—from a pilot prototype to a full-fledged analytical platform or betting service.